Intelligent carrying system and method for stored materials

Abstract

The invention relates to the technical field of material transfer, in particular to a warehouse material intelligent carrying system and method.The warehouse material intelligent carrying system comprises an intelligent dispatching module, an automatic carrying device and a real-time monitoring module; the real-time monitoring module is responsible for collecting real-time data in the carrying process, wherein the real-time data comprise the running state of a conveying belt, the material type and workshop environment parameters. The intelligent scheduling module is responsible for sending a carrying instruction, planning a material carrying route and a scheduling strategy, planning an optimal material carrying route by utilizing an optimization algorithm, and automatically sending the carrying instruction to the automatic carrying device based on a planning result; the automatic carrying device is composed of a conveying belt network, executes a material carrying task according to an instruction of the intelligent dispatching module and is used for carrying materials in a workshop. According to the method, it is ensured that the material carrying path is optimized under various conditions, so that the carrying time is effectively shortened, the cost is reduced, and the risk in the transportation process is minimized.

Description

Technical Field

[0001] The present invention relates to the technical field of material transfer, and in particular to an intelligent storage material transfer system and method. Background Art

[0002] In the logistics and warehousing industry, with the continuous growth of market demand and the improvement of customer service quality requirements, efficient, accurate and reliable material handling systems have become crucial. Traditional material handling methods, such as relying on manual or simple mechanical equipment, can no longer meet the high requirements of modern logistics centers for handling efficiency and accuracy. These traditional methods are not only inefficient, but also prone to material loss and delays due to human errors or mechanical failures, which in turn affects the overall logistics efficiency and service quality.

[0003] With the rapid development of automation technology and information technology, intelligent handling systems have become an effective way to solve the above problems. These systems have realized the automation and intelligence of the material handling process by integrating advanced automated handling equipment, intelligent scheduling algorithms and real-time monitoring technology. However, although the existing intelligent handling systems have improved handling efficiency and reduced human errors to a certain extent, in the complex and changing workshop environment, how to adapt to environmental changes in real time, dynamically optimize the handling path, and ensure the safety and efficiency of handling is still a technical challenge that needs to be solved.

[0004] Especially in the process of material handling and transportation in the workshop, the system needs to be able to flexibly respond to various emergencies, such as conveyor belt failure, path congestion, changes in environmental conditions, etc. This requires the handling system to not only have an efficient handling strategy, but also to have the ability of real-time monitoring and adaptive adjustment. Therefore, the development of a highly intelligent handling system that can comprehensively utilize real-time data, intelligent scheduling and adaptive adjustment of environmental factors is of great significance to improving the operational efficiency and service quality of modern logistics centers. Summary of the invention

[0005] Based on the above objectives, the present invention provides an intelligent storage material handling system and method.

[0006] An intelligent storage material handling system includes an intelligent scheduling module, an automated handling device, and a real-time monitoring module, wherein;

[0007] The real-time monitoring module is responsible for collecting real-time data during the handling process, including the running status of the conveyor belt, the type of materials and the workshop environment parameters;

[0008] The intelligent scheduling module is responsible for issuing handling instructions, planning material handling routes and scheduling strategies, and also integrates adaptive adjustment functions, comprehensively analyzing the data collected by the real-time monitoring module to identify current handling needs and handling bottlenecks, and using optimization algorithms to plan the optimal material handling route, taking into account material types, destinations, priorities, and current handling capacity, and automatically issuing handling instructions to automated handling devices based on planning results;

[0009] The automated handling device is composed of a conveyor belt network, which performs material handling tasks according to the instructions of the intelligent scheduling module and is used for handling materials in a workshop.

[0010] Furthermore, the real-time monitoring module obtains the running speed and direction by controlling the conveyor belt controller, monitors the weight of the material on the conveyor belt through the load sensor, and evaluates the load condition of the conveyor belt. The workshop environmental parameter monitoring includes temperature and humidity monitoring, which monitors the temperature and humidity conditions of the working environment in real time through the temperature and humidity sensors installed in the workshop.

[0011] Furthermore, the optimization algorithm specifically includes:

[0012] Generate an initial transport path population based on the type and destination of the material. Each material is assigned a different path preference based on its characteristics (such as fragile, dangerous goods, etc.) and destination.

[0013] Adjust the priority of the routes in the population according to the urgency or importance of the materials to ensure that high-priority materials are transported to their destination faster;

[0014] Evaluate current handling capacity, including the load and maintenance status of each conveyor belt, to adjust the handling path to avoid congestion and delays;

[0015] A multi-objective optimization method based on genetic algorithms optimizes the transport path in multiple generations through natural selection, crossover and mutation operations, while setting up objective functions and constraints to determine the optimal transport route and scheduling strategy;

[0016] Issue handling instructions: After the optimal handling route is determined, the intelligent scheduling module automatically issues handling instructions to the corresponding automated handling devices to guide the materials to be transported along the selected path.

[0017] Further, the objective function includes minimizing the handling time, minimizing the cost and minimizing the risk, wherein;

[0018] Minimizing the transport time is expressed as: Where T is the total transport time, N is the total number of transport routes, M is the total number of transport tasks, and t ij is the time to execute the jth task through the ith path, x ijis a decision variable. If task j chooses path i, then x ij =1, otherwise 0;

[0019] The minimization cost is expressed as: Where C is the total cost, c ij is the cost of executing the jth task through the i-th path;

[0020] Minimizing risk is expressed as: Where R is the total risk, r ij is the risk assessment value of executing the j-th task through the i-th path.

[0021] Furthermore, the constraints include:

[0022] Only one path can be selected for each task:

[0023] Consider handling capacity limitations: Among them, l j is the handling capacity of the jth task, L i is the maximum carrying capacity of the ith path;

[0024] The genetic algorithm comprises:

[0025] Selection: Select excellent individuals for reproduction based on the fitness function 1 / (T+C+R);

[0026] Crossover: Randomly select chromosome pairs and perform crossover operations to produce new offspring;

[0027] Mutation: Modifying the chromosomes of offspring with a preset probability to introduce new genetic diversity.

[0028] Furthermore, the minimization of the transport time and the minimization of the cost in the objective function also include the consideration of the environmental factor E, and the objective function is updated as follows:

[0029]

[0030] Among them, e ij It reflects the degree of environmental impact on the i-th path when performing the j-th task. The environmental factor E includes the conveyor belt layout, operating status, congestion level of the conveying area, temperature and humidity;

[0031] Integrate real-time feedback mechanism, that is, collect real-time data in the workshop through real-time monitoring module, and dynamically adjust e ij value to reflect the current operating status of the workshop in real time.

[0032] Furthermore, after the objective function is updated, the genetic algorithm is also improved. In each iteration of the algorithm, the fitness evaluation criteria of the individual are adjusted according to real-time feedback, and the transport path that can adapt to the current changes in the workshop environment is preferentially selected. The improved genetic algorithm is as follows:

[0033] Initialization: Generate an initial population, where each individual represents a set of transport path plans;

[0034] Evaluate fitness: Evaluate the fitness of each individual according to the objective function 1 / (T′+C′+R), taking into account transportation time, cost and risk, as well as environmental factors;

[0035] Selection: individuals are selected for reproduction based on fitness;

[0036] Crossover: Randomly select chromosome pairs and perform crossover operations to produce new offspring;

[0037] Mutation: modifying the chromosomes of offspring with a predetermined probability;

[0038] Real-time feedback adjustment: adjust the environmental factor e according to the data of the real-time monitoring module ij , and adjust the fitness evaluation criteria accordingly;

[0039] Iteration: Repeatedly evaluate the fitness to make real-time feedback adjustments until the termination condition is met (such as reaching the preset number of iterations or the fitness no longer improves significantly).

[0040] Furthermore, the generating of the initial population specifically includes:

[0041] Define the encoding scheme: use integer or string encoding, where each gene represents a path selection for a transport task;

[0042] Initialization parameters: population size P, which determines how many transport path solutions there are in the initial population; number of tasks M, the total number of material tasks that need to be transported; path options N, the number of optional paths for each transport task;

[0043] Generate a population: For each individual in the population, repeat the following steps until the population size P is reached:

[0044] aInitialize an empty transport path plan sequence;

[0045] b. For each transport task j (from 1 to M), randomly select a path i (from 1 to N) and add this selection to the transport path solution sequence;

[0046] c Ensure that each selected path is feasible, that is, there is no path that is currently unavailable or has obvious obstacles.

[0047] Furthermore, the conveyor belt network is designed according to the physical layout of the workshop and the transportation requirements, covering all transportation starting points and end points to form a transportation path network. The controller of each conveyor belt adjusts the running direction and speed of the conveyor belt according to the received transportation instructions to guide the material to move along the designated transportation path. When the transportation path involves multiple conveyor belts, the conveyor belts are synchronized and coordinated through unified network communication.

[0048] A method for intelligent handling of storage materials, implemented by the above-mentioned intelligent handling system for storage materials, comprises the following steps:

[0049] S1: Collect real-time data in the workshop, including the running status of the conveyor belt (such as speed, load capacity, fault conditions), material types (weight, size, special handling requirements), and workshop environmental parameters (such as temperature, humidity, and area congestion);

[0050] S2: Comprehensively analyze the collected real-time data, identify the current transportation needs and transportation bottlenecks, and use optimization algorithms to combine material types, destinations, priorities and current transportation capacity factors to plan the optimal material transportation route;

[0051] S3: Dynamically adjust the transportation route and scheduling strategy according to the changes in real-time data;

[0052] S4: Based on the planned optimal transport route, a transport instruction is issued to the automated transport device. After receiving the transport instruction, each conveyor belt adjusts its operating state according to the instruction to perform the material transport task.

[0053] Beneficial effects of the present invention:

[0054] The present invention realizes dynamic optimization and adaptive adjustment of material handling paths by integrating an intelligent scheduling module and an automated conveyor belt network. The intelligent scheduling module utilizes a genetic algorithm to plan the optimal path for each handling task, taking into account the material type, destination, priority, and real-time handling capacity within the workshop. The automated conveyor belt network is flexibly adjusted according to the instructions of the intelligent scheduling center to ensure that materials can be efficiently transported from the starting point to the end point, which not only improves the processing speed and accuracy of the handling tasks, but also enables flexible response to changes in the workshop environment, such as equipment maintenance, congestion, and other emergencies.

[0055] The present invention reduces the reliance on manual operation through the integration of automation and intelligence, thereby reducing labor costs and the possibility of human errors. At the same time, the application of intelligent scheduling and real-time monitoring optimizes the transportation routes and improves the accuracy of transportation operations, further reducing material losses and additional costs caused by misoperation. The highly adaptive capability and real-time feedback mechanism ensure reliability under various operating conditions, reducing downtime caused by equipment failures or other unexpected situations, thereby improving production efficiency while reducing overall operating costs.

[0056] The present invention, an improved genetic algorithm, significantly improves the adaptability and flexibility of the material handling system in a dynamic and complex workshop environment by introducing environmental factor adjustment. This algorithm can not only respond to changes in the internal conditions of the workshop in real time, such as the availability of conveyor belts, the congestion level of the work area and other emergencies, but also dynamically adjust the evaluation criteria of the handling path according to these environmental factors. Therefore, the algorithm can ensure that the material handling path is optimized under various conditions, thereby effectively reducing the handling time, reducing costs, and minimizing the risks during transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 A schematic diagram of system function modules according to an embodiment of the present invention;

[0059] Figure 2 The figure is a schematic diagram of a method flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0061] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0062] like Figure 1 As shown, an intelligent storage material handling system includes an intelligent scheduling module, an automated handling device and a real-time monitoring module, wherein;

[0063] The real-time monitoring module is responsible for collecting real-time data during the handling process, including the operating status of the conveyor belt, material type and workshop environmental parameters;

[0064] The intelligent scheduling module is responsible for issuing handling instructions, planning material handling routes and scheduling strategies, and also integrates adaptive adjustment functions. It comprehensively analyzes the data collected by the real-time monitoring module to identify current handling needs and handling bottlenecks, and uses optimization algorithms to plan the optimal material handling route, taking into account material type, destination, priority, and current handling capacity. Based on the planning results, it automatically issues handling instructions to the automated handling device.

[0065] The automated handling device consists of a conveyor belt network, which performs material handling tasks according to the instructions of the intelligent scheduling module and is used for handling materials within the workshop.

[0066] The real-time monitoring module obtains the running speed and direction by controlling the conveyor belt controller, monitors the weight of the material on the conveyor belt through the load sensor, and evaluates the load condition of the conveyor belt. The workshop environmental parameter monitoring includes temperature and humidity monitoring, which monitors the temperature and humidity conditions of the working environment in real time through the temperature and humidity sensors installed in the workshop.

[0067] The optimization algorithm specifically includes:

[0068] Generate an initial transport path population based on the type and destination of the material. Each material is assigned a different path preference based on its characteristics (such as fragile, dangerous goods, etc.) and destination.

[0069] Adjust the priority of the routes in the population according to the urgency or importance of the materials to ensure that high-priority materials are transported to their destination faster;

[0070] Evaluate current handling capacity, including the load and maintenance status of each conveyor belt, to adjust the handling path to avoid congestion and delays;

[0071] A multi-objective optimization method based on genetic algorithms optimizes the transport path in multiple generations through natural selection, crossover and mutation operations, while setting up objective functions and constraints to determine the optimal transport route and scheduling strategy;

[0072] Issue handling instructions: After the optimal handling route is determined, the intelligent scheduling module automatically issues handling instructions to the corresponding automated handling devices to guide the materials to be transported along the selected path.

[0073] The objective functions include minimizing handling time, minimizing cost, and minimizing risk, where;

[0074] Minimizing the transport time is expressed as: Where T is the total transport time, N is the total number of transport routes, M is the total number of transport tasks, and t ij is the time to execute the jth task through the ith path, x ij is a decision variable. If task j chooses path i, then x ij =1, otherwise 0;

[0075] The minimization cost is expressed as: Where C is the total cost, c ij is the cost of executing the jth task through the i-th path;

[0076] Minimizing risk is expressed as: Where R is the total risk, r ij is the risk assessment value of executing the j-th task through the i-th path.

[0077] The constraints include:

[0078] Only one path can be selected for each task:

[0079] Consider handling capacity limitations: Among them, l j is the handling capacity of the jth task, L i is the maximum carrying capacity of the ith path;

[0080] Genetic algorithms include:

[0081] Selection: Select excellent individuals for reproduction based on the fitness function 1 / (T+C+R);

[0082] Crossover: Randomly select chromosome pairs and perform crossover operations to produce new offspring;

[0083] Mutation: Modifying the chromosomes of offspring with a preset probability to introduce new genetic diversity.

[0084] By iterating these steps, the genetic algorithm can gradually approach the optimal solution over multiple generations. Ultimately, the algorithm will output a set of transport paths that minimize transport time, cost, and risk while satisfying the constraints. The intelligent scheduling module will issue corresponding transport instructions based on these optimal paths.

[0085] The minimization of transportation time and minimization of cost in the objective function also include the environmental factor E, so the objective function is updated as follows:

[0086]

[0087] Among them, e ij It reflects the degree of environmental impact on the i-th path when performing the j-th task. The environmental factor E includes the conveyor belt layout, operating status, congestion level of the conveying area, temperature and humidity;

[0088] Integrate real-time feedback mechanism, that is, collect real-time data in the workshop through real-time monitoring module, and dynamically adjust e ij value to reflect the current operating status of the workshop in real time;.

[0089] Environmental factor E refers to various factors that affect the efficiency and safety of material handling in the workshop. These factors include the physical layout of the workshop, the operating status of the equipment, and the degree of crowding in the work area. The role of environmental factors is to adjust the actual time and cost of the handling task to reflect the actual impact of the current workshop environment on the handling process. Specifically:

[0090] Physical layout: The physical layout of the workshop, such as the layout of the conveyor belts and the location of the workstations, will affect the path selection and length of material handling. A complex or unreasonable layout may cause the handling path to become longer, thereby increasing the handling time.

[0091] Equipment operating status: including the speed and availability of the conveyor belt (whether it is under maintenance or malfunctioning). Unstable operation or malfunction of the equipment will directly affect the handling efficiency and increase the handling time and cost.

[0092] Crowding degree of the working area: The congestion of human and logistics flow within the workshop will affect the smoothness of the handling operation. Highly crowded areas will cause obstruction of material handling, increase waiting time and handling risks.

[0093] Environmental conditions: Temperature and humidity may affect the handling of certain sensitive materials, requiring special handling conditions and speeds, which in turn affects handling time and cost.

[0094] After the objective function is updated, the genetic algorithm is also improved. In each iteration of the algorithm, the individual fitness evaluation criteria are adjusted according to real-time feedback, and the transport path that can adapt to the current changes in the workshop environment is given priority. The improved genetic algorithm is as follows:

[0095] 1. Initialization: Randomly generate a certain number of individuals as the initial population, each of which represents a set of transport path plans. Each plan includes the path arrangement for all transport tasks.

[0096] 2. Evaluate fitness: Calculate the fitness of each individual and evaluate the fitness of each individual according to the objective function F = α / T′+β / C′+γ / R, where T′, C′ and R are the transportation time, cost and risk adjusted for environmental factors, respectively, and α, β and γ are weight coefficients used to balance the importance of different goals.

[0097] 3. Selection: Based on the roulette wheel selection method, selection is made according to the fitness of individuals. Individuals with high fitness are more likely to be selected, but a certain chance is also reserved for individuals with lower fitness to maintain the diversity of the population.

[0098] 4. Crossover: Randomly select two parent individuals, then randomly select a crossover point, exchange the gene sequence after this point, and generate two new offspring individuals. Consider using multi-point crossover or uniform crossover technology to increase the diversity of the population.

[0099] 5. Mutation: Randomly change certain genes of individuals (i.e., transport path selection) with a lower probability to introduce new genetic variation and increase the diversity of the population.

[0100] 6. Real-time feedback adjustment:

[0101] Environmental factor adjustment: Dynamically adjust the environmental factor of each transport path based on the workshop environment and equipment status information provided by the real-time monitoring system. ij , to reflect the impact of the current internal environment of the workshop on the handling task.

[0102] Fitness function update: According to the adjustment of environmental factors, the fitness function is updated to ensure that the algorithm can adapt to changes in the workshop environment in real time.

[0103] 7. Elite retention strategy:

[0104] Elite selection: A certain number of the best individuals are retained in each generation and passed directly to the next generation to ensure that the algorithm can retain the optimal solution that has been discovered.

[0105] 8. Iteration: Repeat steps 2-7 until the termination condition is met, such as reaching the preset number of iterations or the average fitness of the population no longer improves significantly.

[0106] 9. Output the optimal solution:

[0107] Select the best individual: Select the individual with the highest fitness from the final population as the optimal transportation path plan.

[0108] Apply the optimal solution: Apply the optimal handling path solution to the intelligent scheduling module to guide the actual material handling operations.

[0109] In the workshop material handling and conveying system of the present invention, the improved genetic algorithm can effectively adapt to the dynamic changes within the workshop. Through the real-time feedback mechanism, the intelligent scheduling module can update the priority and strategy of the handling path in real time to ensure that efficient and safe handling operations can be maintained in a changing environment. In this way, even in the face of emergencies or unpredictable environmental changes, the system can respond flexibly to minimize the impact on handling efficiency and safety.

[0110] In the improved genetic algorithm, the environmental factor e ij It is used to adjust the transportation time and cost of the i-th path when performing the j-th task. For example, if a path passes through a conveyor belt that is under maintenance, the transportation time of this path will increase, which can be achieved by increasing the e of this path. ij Similarly, if a route passes through a congested area, the transportation cost and risk of this route may increase, and the corresponding e ij The value will also increase.

[0111] Generating the initial population specifically includes:

[0112] Define the encoding scheme: use integer or string encoding, where each gene represents a path choice for a transport task. For example, if there are three possible paths and five transport tasks, each individual can be represented by a sequence of length five, where each number in the sequence is between 1 and 3, representing the path chosen for the corresponding task.

[0113] Initialization parameters: population size P, which determines how many transport path solutions there are in the initial population. It is set according to the scale and complexity of the problem. A larger population can provide more diversity, but it will also increase the computational cost; number of tasks M, the total number of material tasks that need to be transported; path options N, the number of optional paths for each transport task;

[0114] Generate a population: For each individual in the population, repeat the following steps until the population size P is reached:

[0115] aInitialize an empty transport path plan sequence;

[0116] b. For each transport task j (from 1 to M), randomly select a path i (from 1 to N) and add this selection to the transport path solution sequence;

[0117] c. Ensure that each selected path is feasible, that is, there is no path that is currently unavailable or has obvious obstacles;

[0118] When generating the initial population, ensuring the diversity of the population, that is, avoiding too many repeated transport path plans, can be achieved by checking whether the newly generated transport path plan is too similar to the existing plan. If so, regenerate it. For each generated transport path plan, perform a quick feasibility check to ensure that each path selection in the plan meets the actual transportation conditions. For example, some paths may not be available at a specific time due to maintenance, congestion or other reasons.

[0119] The conveyor belt network is designed according to the physical layout of the workshop and the handling requirements, covering all handling starting points and end points to form a handling path network. The controller of each conveyor belt adjusts the running direction and speed of the conveyor belt according to the received handling instructions to guide the material to move along the designated handling path. When the handling path involves multiple conveyor belts, the conveyor belts are synchronized and coordinated through unified network communication.

[0120] like Figure 2 As shown, a method for intelligent handling of storage materials is implemented by the above-mentioned intelligent handling system for storage materials, and includes the following steps:

[0121] S1: Collect real-time data in the workshop, including the running status of the conveyor belt (such as speed, load capacity, fault conditions), material types (weight, size, special handling requirements), and workshop environmental parameters (such as temperature, humidity, and area congestion);

[0122] S2: Comprehensively analyze the collected real-time data, identify the current transportation needs and transportation bottlenecks, and use optimization algorithms to combine material types, destinations, priorities and current transportation capacity factors to plan the optimal material transportation route;

[0123] S3: Dynamically adjust the transportation route and scheduling strategy according to the changes in real-time data;

[0124] S4: Based on the planned optimal transport route, a transport instruction is issued to the automated transport device. After receiving the transport instruction, each conveyor belt adjusts its operating state according to the instruction to perform the material transport task.

[0125] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention is limited to these examples. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the above aspects of the present invention, which are not provided in detail for the sake of simplicity.

[0126] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent storage material handling system, characterized in that: It includes intelligent scheduling module, automated handling device and real-time monitoring module, among which; The real-time monitoring module is responsible for collecting real-time data during the handling process, including the running status of the conveyor belt, the type of materials and the workshop environmental parameters. The real-time monitoring module obtains the running speed and direction by controlling the controller of the conveyor belt, monitors the weight of the materials on the conveyor belt through the load sensor, and evaluates the load condition of the conveyor belt. The workshop environmental parameter monitoring includes temperature and humidity monitoring, which monitors the temperature and humidity conditions of the working environment in real time through the temperature and humidity sensors installed in the workshop; The intelligent scheduling module is responsible for issuing handling instructions, planning material handling routes and scheduling strategies, and also integrates adaptive adjustment functions, comprehensively analyzing the data collected by the real-time monitoring module to identify current handling needs and handling bottlenecks, and using optimization algorithms to plan the optimal material handling routes, taking into account material types, destinations, priorities, and current handling capabilities, and automatically issuing handling instructions to automated handling devices based on planning results. The optimization algorithms specifically include: Generate an initial transport path population based on the type and destination of the material. Each material is assigned a different path preference based on its characteristics and destination. Adjust the priority of the routes in the population according to the urgency or importance of the materials to ensure that high-priority materials are transported to their destination faster; Evaluate current handling capacity, including the load and maintenance status of each conveyor belt, to adjust the handling path to avoid congestion and delays; A multi-objective optimization method based on genetic algorithms optimizes the transport path in multiple generations through natural selection, crossover and mutation operations, while setting up objective functions and constraints to determine the optimal transport route and scheduling strategy; Issue handling instructions: After the optimal handling route is determined, the intelligent scheduling module automatically issues handling instructions to the corresponding automated handling devices to guide the materials to be transported along the selected route; The objective function includes minimizing handling time, minimizing cost and minimizing risk, wherein; Minimizing the transport time is expressed as: Where T is the total transport time, N is the total number of transport routes, M is the total number of transport tasks, and t ij is the time to execute the jth task through the ith path, x ij is a decision variable. If task j chooses path i, then x ij =1, otherwise 0; The minimization cost is expressed as: Where C is the total cost, c ij is the cost of executing the jth task through the i-th path; Minimizing risk is expressed as: Where R is the total risk, r ij is the risk assessment value of executing the jth task through the i-th path; The constraints include: Only one path can be selected for each task: Consider handling capacity limitations: Among them, l j is the handling capacity of the jth task, L i is the maximum carrying capacity of the ith path; The genetic algorithm comprises: Selection: Select excellent individuals for reproduction based on the fitness function 1 / (T+C+R); Crossover: Randomly select chromosome pairs and perform crossover operations to produce new offspring; Mutation: Modify the chromosomes of offspring with a preset probability to introduce new genetic diversity; The minimization of transportation time and minimization of cost in the objective function also include consideration of environmental factor E, so the objective function is updated as follows: Among them, e ij It reflects the degree of environmental impact on the i-th path when performing the j-th task. The environmental factor E includes the conveyor belt layout, operating status, congestion level of the conveying area, temperature and humidity; Integrate real-time feedback mechanism, that is, collect real-time data in the workshop through real-time monitoring module, and dynamically adjust e ij The value of to reflect the current operation status of the workshop in real time; After the objective function is updated, the genetic algorithm is also improved. In each iteration of the algorithm, the fitness evaluation criteria of the individual are adjusted according to real-time feedback, and the transport path that can adapt to the current changes in the workshop environment is preferentially selected. The improved genetic algorithm is as follows: Initialization: Generate an initial population, where each individual represents a set of transport path plans; Evaluate fitness: Evaluate the fitness of each individual according to the objective function 1 / (T′+C′+R), taking into account transportation time, cost and risk, as well as environmental factors; Selection: individuals are selected for reproduction based on fitness; Crossover: Randomly select chromosome pairs and perform crossover operations to produce new offspring; Mutation: modifying the chromosomes of offspring with a predetermined probability; Real-time feedback adjustment: adjust the environmental factor e according to the data of the real-time monitoring module ij , and adjust the fitness evaluation criteria accordingly; Iteration: Repeatedly evaluate fitness and make real-time feedback adjustments until the termination condition is met; The generating of the initial population specifically comprises: Define the encoding scheme: use integer or string encoding, where each gene represents a path selection for a transport task; Initialization parameters: population size P, which determines how many transport path solutions there are in the initial population; number of tasks <, the total number of material tasks that need to be transported; path options N, the number of optional paths for each transport task; Generate a population: For each individual in the population, repeat the following steps until the population size P is reached: aInitialize an empty transport path plan sequence; b For each transport task j, randomly select a path i and add this selection to the transport path solution sequence; c. Ensure that each selected path is feasible, that is, there is no path that is currently unavailable or has obvious obstacles; The automated handling device is composed of a conveyor belt network, which performs material handling tasks according to the instructions of the intelligent scheduling module and is used for handling materials within the workshop; the conveyor belt network is designed according to the physical layout and handling requirements of the workshop, covers all handling starting points and end points, and forms a handling path network. The controller of each conveyor belt adjusts the running direction and speed of the conveyor belt according to the received handling instructions, and guides the material to move along the designated handling path. When the handling path involves multiple conveyor belts, the conveyor belts are synchronized and coordinated through unified network communication.

2. A method for intelligent handling of storage materials, implemented by the intelligent handling system for storage materials according to claim 1, characterized in that: The following steps are involved: S1: Collect real-time data in the workshop, including the running status of the conveyor belt, material types and workshop environmental parameters; S2: Comprehensively analyze the collected real-time data, identify the current transportation needs and transportation bottlenecks, and use optimization algorithms to combine material types, destinations, priorities and current transportation capacity factors to plan the optimal material transportation route; S3: Dynamically adjust the transportation route and scheduling strategy according to the changes in real-time data; S4: Based on the planned optimal transport route, a transport instruction is issued to the automated transport device. After receiving the transport instruction, each conveyor belt adjusts its operating state according to the instruction to perform the material transport task.

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